% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/gpb.cv.R
\name{gpb.grid.search.tune.parameters}
\alias{gpb.grid.search.tune.parameters}
\title{Function for choosing tuning parameters}
\usage{
gpb.grid.search.tune.parameters(param_grid, num_try_random = NULL, data,
  gp_model = NULL, params = list(), nrounds = 1000L,
  early_stopping_rounds = NULL, folds = NULL, nfold = 5L, metric = NULL,
  verbose_eval = 1L, cv_seed = NULL, line_search_step_length = FALSE,
  use_gp_model_for_validation = TRUE, train_gp_model_cov_pars = TRUE,
  label = NULL, weight = NULL, obj = NULL, eval = NULL,
  stratified = TRUE, init_model = NULL, colnames = NULL,
  categorical_feature = NULL, callbacks = list(),
  return_all_combinations = FALSE, ...)
}
\arguments{
\item{param_grid}{\code{list} with candidate parameters defining the grid over which a search is done}

\item{num_try_random}{\code{integer} with number of random trial on parameter grid. If NULL, a deterministic search is done}

\item{data}{a \code{gpb.Dataset} object, used for training. Some functions, such as \code{\link{gpb.cv}},
may allow you to pass other types of data like \code{matrix} and then separately supply
\code{label} as a keyword argument.}

\item{gp_model}{A \code{GPModel} object that contains the random effects (Gaussian process and / or grouped random effects) model}

\item{params}{\code{list} with other parameters not included in \code{param_grid}}

\item{nrounds}{number of boosting iterations (= number of trees). This is the most important tuning parameter for boosting}

\item{early_stopping_rounds}{int. Activates early stopping. Requires at least one validation data
and one metric. When this parameter is non-null,
training will stop if the evaluation of any metric on any validation set
fails to improve for \code{early_stopping_rounds} consecutive boosting rounds.
If training stops early, the returned model will have attribute \code{best_iter}
set to the iteration number of the best iteration.}

\item{folds}{\code{list} provides a possibility to use a list of pre-defined CV folds
(each element must be a vector of test fold's indices). When folds are supplied,
the \code{nfold} and \code{stratified} parameters are ignored.}

\item{nfold}{the original dataset is randomly partitioned into \code{nfold} equal size subsamples.}

\item{metric}{Evaluation metric to be monitored when doing CV and parameter tuning. 
Can be a \code{character} string or vector of \code{character} strings.
If not NULL, the metric in \code{params} will be overridden.
Non-exhaustive list of supported metrics: "test_neg_log_likelihood", "mse", "rmse", "mae", 
"auc", "average_precision", "binary_logloss", "binary_error". 
See \href{https://gpboost.readthedocs.io/en/latest/Parameters.html#metric-parameters}{
the "metric" section of the parameter documentation} for a complete list of valid metrics.}

\item{verbose_eval}{\code{integer}. Whether to display information on the progress of tuning parameter choice. 
If None or 0, verbose is of.
If = 1, summary progress information is displayed for every parameter combination.
If >= 2, detailed progress is displayed at every boosting stage for every parameter combination.}

\item{cv_seed}{Seed for generating folds when doing \code{nfold} CV}

\item{line_search_step_length}{Boolean. If TRUE, a line search is done to find the optimal step length for every boosting update 
(see, e.g., Friedman 2001). This is then multiplied by the \code{learning_rate}. 
Applies only to the GPBoost algorithm}

\item{use_gp_model_for_validation}{Boolean. If TRUE, the \code{gp_model} 
(Gaussian process and/or random effects) is also used (in addition to the tree model) for calculating 
predictions on the validation data. If FALSE, the \code{gp_model} (random effects part) is ignored 
for making predictions and only the tree ensemble is used for making predictions for calculating the validation / test error.}

\item{train_gp_model_cov_pars}{Boolean. If TRUE, the covariance parameters 
of the \code{gp_model} (Gaussian process and/or random effects) are estimated in every 
boosting iterations, otherwise the \code{gp_model} parameters are not estimated. 
In the latter case, you need to either estimate them beforehand or provide the values via 
the \code{init_cov_pars} parameter when creating the \code{gp_model}}

\item{label}{Vector of labels, used if \code{data} is not an \code{\link{gpb.Dataset}}}

\item{weight}{vector of response values. If not NULL, will set to dataset}

\item{obj}{(character) The distribution of the response variable (=label) conditional on fixed and random effects.
This only needs to be set when doing independent boosting without random effects / Gaussian processes.}

\item{eval}{Evaluation metric to be monitored when doing CV and parameter tuning. 
This can be a string, function, or list with a mixture of strings and functions.

            \itemize{
                \item{\bold{a. character vector}:
                    Non-exhaustive list of supported metrics: "test_neg_log_likelihood", "mse", "rmse", "mae", 
                    "auc", "average_precision", "binary_logloss", "binary_error"
                    See \href{https://gpboost.readthedocs.io/en/latest/Parameters.html#metric-parameters}{
                    the "metric" section of the parameter documentation}
                    for a complete list of valid metrics.
                }
                \item{\bold{b. function}:
                     You can provide a custom evaluation function. This
                     should accept the keyword arguments \code{preds} and \code{dtrain} and should return a named
                     list with three elements:
                     \itemize{
                         \item{\code{name}: A string with the name of the metric, used for printing
                             and storing results.
                         }
                         \item{\code{value}: A single number indicating the value of the metric for the
                             given predictions and true values
                         }
                         \item{
                             \code{higher_better}: A boolean indicating whether higher values indicate a better fit.
                             For example, this would be \code{FALSE} for metrics like MAE or RMSE.
                         }
                     }
                }
                \item{\bold{c. list}:
                    If a list is given, it should only contain character vectors and functions.
                    These should follow the requirements from the descriptions above.
                }
            }}

\item{stratified}{a \code{boolean} indicating whether sampling of folds should be stratified
by the values of outcome labels.}

\item{init_model}{path of model file of \code{gpb.Booster} object, will continue training from this model}

\item{colnames}{feature names, if not null, will use this to overwrite the names in dataset}

\item{categorical_feature}{categorical features. This can either be a character vector of feature
names or an integer vector with the indices of the features (e.g.
\code{c(1L, 10L)} to say "the first and tenth columns").}

\item{callbacks}{List of callback functions that are applied at each iteration.}

\item{return_all_combinations}{a \code{boolean} indicating whether all tried 
parameter combinations are returned}

\item{...}{other parameters, see Parameters.rst for more information.}
}
\value{
A \code{list} with the best parameter combination and score
The list has the following format:
 list("best_params" = best_params, "best_iter" = best_iter, "best_score" = best_score)
 If return_all_combinations is TRUE, then the list contains an additional entry 'all_combinations'
}
\description{
Function that allows for choosing tuning parameters from a grid in a determinstic or random way using cross validation or validation data sets.
}
\section{Early Stopping}{


         "early stopping" refers to stopping the training process if the model's performance on a given
         validation set does not improve for several consecutive iterations.

         If multiple arguments are given to \code{eval}, their order will be preserved. If you enable
         early stopping by setting \code{early_stopping_rounds} in \code{params}, by default all
         metrics will be considered for early stopping.

         If you want to only consider the first metric for early stopping, pass
         \code{first_metric_only = TRUE} in \code{params}. Note that if you also specify \code{metric}
         in \code{params}, that metric will be considered the "first" one. If you omit \code{metric},
         a default metric will be used based on your choice for the parameter \code{obj} (keyword argument)
         or \code{objective} (passed into \code{params}).
}

\examples{
# See https://github.com/fabsig/GPBoost/tree/master/R-package for more examples
\donttest{
library(gpboost)
data(GPBoost_data, package = "gpboost")

n <- length(y)
param_grid <- list("learning_rate" = c(0.001, 0.01, 0.1, 1, 10), 
                   "min_data_in_leaf" = c(1, 10, 100, 1000),
                   "max_depth" = c(-1), 
                   "num_leaves" = 2^(1:10),
                   "lambda_l2" = c(0, 1, 10, 100),
                   "max_bin" = c(250, 500, 1000, min(n,10000)),
                   "line_search_step_length" = c(TRUE, FALSE))
# Note: "max_depth" = c(-1) means no depth limit as we tune 'num_leaves'. 
#    Can also additionally tune 'max_depth', e.g., "max_depth" = c(-1, 1, 2, 3, 5, 10)
metric = "mse" # Define metric
# Note: can also use metric = "test_neg_log_likelihood". 
# See https://github.com/fabsig/GPBoost/blob/master/docs/Parameters.rst#metric-parameters
gp_model <- GPModel(group_data = group_data[,1], likelihood="gaussian")
data_train <- gpb.Dataset(data = X, label = y)
set.seed(1)
opt_params <- gpb.grid.search.tune.parameters(param_grid = param_grid,
                                              data = data_train, gp_model = gp_model,
                                              num_try_random = 100, nfold = 5,
                                              nrounds = 1000, early_stopping_rounds = 20,
                                              verbose_eval = 1, metric = metric, cv_seed = 4)
print(paste0("Best parameters: ",
             paste0(unlist(lapply(seq_along(opt_params$best_params), 
                                  function(y, n, i) { paste0(n[[i]],": ", y[[i]]) }, 
                                  y=opt_params$best_params, 
                                  n=names(opt_params$best_params))), collapse=", ")))
print(paste0("Best number of iterations: ", opt_params$best_iter))
print(paste0("Best score: ", round(opt_params$best_score, digits=3)))
# Alternatively and faster: using manually defined validation data instead of cross-validation
# use 20\% of the data as validation data
valid_tune_idx <- sample.int(length(y), as.integer(0.2*length(y))) 
folds <- list(valid_tune_idx)
opt_params <- gpb.grid.search.tune.parameters(param_grid = param_grid,
                                              data = data_train, gp_model = gp_model,
                                              num_try_random = 100, folds = folds,
                                              nrounds = 1000, early_stopping_rounds = 20,
                                              verbose_eval = 1, metric = metric, cv_seed = 4)

}
}
\author{
Fabio Sigrist
}
